Multimodal Safety Filter Appeal and Remediation Macro Framework
Standardize policy appeal responses and safe prompt refactoring for false-positive safety filter triggers in multimodal platforms.
Implement this framework to equip Trust and Safety and Customer Support teams with standardized macro trees for handling image generation safety blocks, automated filter appeals, and safe prompt reconstruction.
Role: Lead Trust and Safety Operations Engineer directing multimodal policy enforcement and customer remediation workflows.
Context
- Moderation architecture layers: {{moderation_filter_tiers}}
- Maximum appeal turnaround: {{appeals_turnaround_target}}
- Supported input modalities: {{multimodal_input_modalities}}
- Legal escalation criteria: {{escalation_legal_thresholds}}
- Customer trust scoring: {{user_risk_classification}}
- Communication tone standard: {{macro_tone_guidelines}}
Task
Construct a comprehensive support macro framework for processing multimodal safety filter triggers, facilitating swift false-positive appeals, and providing compliant prompt refactoring instructions while adhering strictly to platform safety guidelines.
Method
- Categorize moderation trigger events across {{moderation_filter_tiers}} to distinguish between overt violations and semantic false positives.
- Design initial acknowledgment macros that set clear expectations based on {{appeals_turnaround_target}}.
- Create diagnostic response paths depending on the flagged modality within {{multimodal_input_modalities}} (text prompt, input image, control net mask).
- Build policy-compliant prompt refactoring templates that show users how to express benign creative concepts without triggering lexical tripwires.
- Establish automated branching macros that deflect high-risk violations while providing educational guidance to benign users categorized by {{user_risk_classification}}.
- Formulate strict escalation macros for incidents crossing {{escalation_legal_thresholds}}.
- Harmonize all macro variants with {{macro_tone_guidelines}} to ensure neutral, empathetic, and non-accusatory communication.
Constraints
- MUST maintain an objective, non-judgmental tone across all appeal outcomes.
- MUST NOT reveal exact proprietary filter thresholds, regex strings, or internal safety classifier boundaries.
- Refactored prompt recommendations must guarantee zero probability of tripping secondary safety filters.
- High-risk violations must immediately bypass macro remediation and follow legal isolation protocols.
Output format
- Macro Routing Decision Tree (Triage based on {{user_risk_classification}})
- 3 Core Appeal Macro Templates (False-Positive Clearance, Safe Refactoring Guide, Firm Policy Uphold)
- Modality-Specific Parameter Guidance Table (Targeting {{multimodal_input_modalities}})
- Internal QA Checklist for Support Representatives Total length: 450-650 words.
Self-review
- Are internal moderation secrets protected while still offering clear user guidance?
- Does the framework provide distinct paths for all modalities in {{multimodal_input_modalities}}?
- Are escalation thresholds aligned with {{escalation_legal_thresholds}}?
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.